G4 Pattern Discovery.
Our expert interviews and experiments exposed that, in large label spaces, domain experts need to be able to spot systematically biased label groups and differentiate these from individual labels that expose problematic bias.
Thus, we propose to visualize patterns and outliers in the bias-distribution of the data.
We argue that visualizations for large label spaces should incorporate and transform label similarity information into visualizations that expose biases within the context of label similarity.
This approach is similar to insights on semantic information extraction from the domain of natural language processing [1]}.
